{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6e1fcab",
   "metadata": {},
   "source": [
    "|任务名称|难度/分值|\n",
    "|--|--|\n",
    "|任务1：数据读取与分析|低/1|\n",
    "|任务2：TFIDF提取与分类|中/2|\n",
    "|任务3：词向量训练与使用|中/2|\n",
    "|任务4：LSTM意图分类|高/3|\n",
    "|任务5：BERT意图分类|高/3|\n",
    "|任务6：T5/Prompt意图分类|高/3|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aef7246a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import jieba\n",
    "import jieba.analyse as ana\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score,f1_score\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "from gensim.models import Word2Vec\n",
    "import gensim\n",
    "\n",
    "import joblib\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b13eb4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>还有双鸭山到淮阴的汽车票吗13号的</td>\n",
       "      <td>Travel-Query</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>从这里怎么回家</td>\n",
       "      <td>Travel-Query</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>随便播放一首专辑阁楼里的佛里的歌</td>\n",
       "      <td>Music-Play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>给看一下墓王之王嘛</td>\n",
       "      <td>FilmTele-Play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>我想看挑战两把s686打突变团竞的游戏视频</td>\n",
       "      <td>Video-Play</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    text          label\n",
       "0      还有双鸭山到淮阴的汽车票吗13号的   Travel-Query\n",
       "1                从这里怎么回家   Travel-Query\n",
       "2       随便播放一首专辑阁楼里的佛里的歌     Music-Play\n",
       "3              给看一下墓王之王嘛  FilmTele-Play\n",
       "4  我想看挑战两把s686打突变团竞的游戏视频     Video-Play"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('data/train.csv', sep='\\t', header=None)\n",
    "test_df = pd.read_csv('data/test.csv',sep='\\t', header=None)\n",
    "\n",
    "train_df = train_df.rename(columns = {0:'text', 1:'label'})\n",
    "test_df = test_df.rename(columns = {0:'text'})\n",
    "\n",
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbbdf9a4",
   "metadata": {},
   "source": [
    "# 任务1：数据读取与分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d01795",
   "metadata": {},
   "source": [
    "数据链接: https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi 提取码: v4bi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f110730d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FilmTele-Play            1355\n",
       "Video-Play               1334\n",
       "Music-Play               1304\n",
       "Radio-Listen             1285\n",
       "Alarm-Update             1264\n",
       "Weather-Query            1229\n",
       "Travel-Query             1220\n",
       "HomeAppliance-Control    1215\n",
       "Calendar-Query           1214\n",
       "TVProgram-Play            240\n",
       "Audio-Play                226\n",
       "Other                     214\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计label分布\n",
    "train_df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "64c225b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 四分位: [9.0, 15.0, 21.0, 24.0]  均值: 15.138677685950412\n",
      "test: 四分卫 [10.0, 15.0, 23.0, 25.0]  均值: 15.884333333333334\n"
     ]
    }
   ],
   "source": [
    "# 统计数据集字符长度\n",
    "train_df['len'] = train_df.apply(lambda row: len(row['text']), axis=1)\n",
    "test_df['len'] = test_df.apply(lambda row: len(row['text']), axis=1)\n",
    "\n",
    "print('train 四分位:', train_df['len'].quantile([0.1, 0.5, 0.9, 0.95]).tolist(), ' 均值:', train_df['len'].mean())\n",
    "print('test: 四分卫', test_df['len'].quantile([0.1, 0.5, 0.9, 0.95]).tolist(), ' 均值:', test_df['len'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8e74ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 合并数据集，统计词云\n",
    "df = pd.concat([train_df, test_df])\n",
    "words = []\n",
    "for idx, row in df.iterrows():\n",
    "    words += jieba.cut(row['text'])\n",
    "words = ' '.join(list(set(words)))\n",
    "wc = WordCloud(mode=\"RGBA\", background_color=None).generate(words)\n",
    "# 显示词云图\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac72e954",
   "metadata": {},
   "source": [
    "#  任务2：TFIDF提取与分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef22e169",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备数据\n",
    "texts = train_df['text'].tolist()\n",
    "labels = train_df['label'].tolist()\n",
    "# 将文本向量化\n",
    "vectorizer = TfidfVectorizer(max_df=0.8, ngram_range=(1,2))\n",
    "X = vectorizer.fit_transform(texts)\n",
    "y = labels\n",
    "# 将数据拆分为训练和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74474b28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- knn ----------\n",
      "Accuracy: 0.1231404958677686\n",
      "Precision: 0.518716976842223\n",
      "Recall: 0.1231404958677686\n",
      "F1-Score: 0.06204912295080266\n",
      "---------- svm ----------\n",
      "Accuracy: 0.13512396694214876\n",
      "Precision: 0.7653215365444758\n",
      "Recall: 0.13512396694214876\n",
      "F1-Score: 0.05643010984722885\n",
      "---------- lr ----------\n",
      "Accuracy: 0.1347107438016529\n",
      "Precision: 0.7429953215441332\n",
      "Recall: 0.1347107438016529\n",
      "F1-Score: 0.05559933934423648\n"
     ]
    }
   ],
   "source": [
    "models = {'knn':KNeighborsClassifier(), 'svm':LinearSVC(), 'lr':LogisticRegression()}\n",
    "for k, clf in models.items():\n",
    "    print('-'*10,k,'-'*10,)\n",
    "    clf.fit(X_train, y_train)\n",
    "    # 测试模型\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "    print('Precision:', precision_score(y_test, y_pred, average='weighted'))\n",
    "    print('Recall:', recall_score(y_test, y_pred, average='weighted'))\n",
    "    print('F1-Score:', f1_score(y_test, y_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8779c4",
   "metadata": {},
   "source": [
    "# 任务3：词向量训练与使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "75b9148b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_size=100\n",
    "\n",
    "def get_words(seq, stop_words):\n",
    "    # 分词\n",
    "    seg_list = jieba.cut(seq)  # 分词\n",
    "    # 转列表\n",
    "    words  = ' '.join(seg_list).split(' ')\n",
    "    # 去除停用词&返回\n",
    "    return words\n",
    "\n",
    "# 加载停用词表\n",
    "stop_words = pd.read_csv('baidu_stopwords.txt', header=None)[0].tolist()\n",
    "# 拼接\n",
    "df = pd.concat([train_df, test_df])\n",
    "# 分词\n",
    "df['words'] = df['text'].apply(lambda x: get_words(x,stop_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bef31755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done.\n"
     ]
    }
   ],
   "source": [
    "# 训练词向量\n",
    "wv_model = Word2Vec(sentences=df['words'].tolist(), vector_size=vector_size, window=5, min_count=1, workers=4)\n",
    "wv_model.save(\"word2vec.model\")\n",
    "print('done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5aa57649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取句向量\n",
    "def get_seq_vector(words, wv_model):\n",
    "    word_vector_list = []\n",
    "    for word in words:\n",
    "        try:\n",
    "            word_vector_list.append(wv_model.wv[word])\n",
    "        except Exception as ex:\n",
    "            print(1)\n",
    "            continue\n",
    "    return np.mean(word_vector_list, axis=0)\n",
    "\n",
    "df['sv'] = df['words'].apply(lambda x: get_seq_vector(x, wv_model)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2d64eae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------- knn ----------\n",
      "Accuracy: 0.7115702479338843\n",
      "Precision: 0.7146919233546835\n",
      "Recall: 0.7115702479338843\n",
      "F1-Score: 0.7109965798921437\n",
      "---------- lr ----------\n",
      "Accuracy: 0.7694214876033058\n",
      "Precision: 0.7591080687265356\n",
      "Recall: 0.7694214876033058\n",
      "F1-Score: 0.7550596684188562\n",
      "---------- svm ----------\n",
      "Accuracy: 0.8037190082644629\n",
      "Precision: 0.7607319613987127\n",
      "Recall: 0.8037190082644629\n",
      "F1-Score: 0.7810045733698987\n"
     ]
    }
   ],
   "source": [
    "new_train_df = df[~df['label'].isna()]\n",
    "new_test_df = df[df['label'].isna()]\n",
    "\n",
    "new_train_df = new_train_df.dropna()\n",
    "# 准备数据\n",
    "X = [i.tolist() for i in new_train_df['sv'].tolist()]\n",
    "y = new_train_df['label'].tolist()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 训练模型\n",
    "models = {'knn':KNeighborsClassifier(), 'lr':LogisticRegression(), 'svm':LinearSVC()}\n",
    "for k, clf in models.items():\n",
    "    print('-'*10,k,'-'*10,)\n",
    "    clf.fit(np.array(X_train), y_train)\n",
    "    # 测试模型\n",
    "    y_pred = clf.predict(X_test)\n",
    "    print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "    print('Precision:', precision_score(y_test, y_pred, average='weighted'))\n",
    "    print('Recall:', recall_score(y_test, y_pred, average='weighted'))\n",
    "    print('F1-Score:', f1_score(y_test, y_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "53636850",
   "metadata": {},
   "outputs": [],
   "source": [
    "r = clf.predict([i.tolist() for i in new_test_df['sv'].tolist()])\n",
    "result_df = pd.DataFrame()\n",
    "result_df['ID'] = [i for i in range(len(new_test_df))]\n",
    "result_df['Target'] = r\n",
    "result_df.to_csv('result/w2v_svm.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f241a772",
   "metadata": {},
   "source": [
    "# 任务4：LSTM意图分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "891f6f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_words(seq, stop_words):\n",
    "    # 分词\n",
    "    seg_list = jieba.cut(seq)  # 分词\n",
    "    # 转列表\n",
    "    words  = ' '.join(seg_list).split(' ')\n",
    "    # 去除停用词&返回\n",
    "    return [i for i in words if  i not in stop_words]\n",
    "\n",
    "# 加载停用词表\n",
    "stop_words = pd.read_csv('baidu_stopwords.txt', header=None)[0].tolist()\n",
    "# 统计词的长度\n",
    "df = pd.concat([train_df, test_df])\n",
    "df['words'] = df['text'].apply(lambda x: get_words(x,stop_words))\n",
    "df['words_len'] = df['words'].apply(lambda x: len(x))\n",
    "print('train 四分位:', df['words_len'].quantile([0.1, 0.5, 0.9, 0.95]).tolist(), ' 均值:', df['words_len'].mean())\n",
    "# 生成词典\n",
    "word2vec = Word2Vec.load(config.wv_model).wv #加载任务3的词向量\n",
    "wordswords = wv_model.wv.index_to_key\n",
    "\n",
    "for idx, row in df.iterrows():\n",
    "    words += row['words']\n",
    "\n",
    "words = list(set(words))\n",
    "i = 1\n",
    "vocab_dict = {'[pad]':0}\n",
    "for w in words:\n",
    "    vocab_dict[w] = i\n",
    "    i+=1\n",
    "joblib.dump(vocab_dict, filename='./vocab.pkl')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36cb4cc",
   "metadata": {},
   "source": [
    "# 任务5：BERT意图分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1772802b",
   "metadata": {},
   "source": [
    "见BERTPaddle.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2896a2cf",
   "metadata": {},
   "source": [
    "# 任务6：T5/Prompt意图分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "745f8fd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[2023-03-14 11:40:54,202] [    INFO]\u001b[0m - We are using <class 'paddlenlp.transformers.ernie.tokenizer.ErnieTokenizer'> to load 'ernie-3.0-base-zh'.\u001b[0m\n",
      "\u001b[32m[2023-03-14 11:40:54,218] [    INFO]\u001b[0m - Already cached D:\\env\\bert_model\\models\\ernie-3.0-base-zh\\ernie_3.0_base_zh_vocab.txt\u001b[0m\n",
      "\u001b[32m[2023-03-14 11:40:54,238] [    INFO]\u001b[0m - tokenizer config file saved in D:\\env\\bert_model\\models\\ernie-3.0-base-zh\\tokenizer_config.json\u001b[0m\n",
      "\u001b[32m[2023-03-14 11:40:54,238] [    INFO]\u001b[0m - Special tokens file saved in D:\\env\\bert_model\\models\\ernie-3.0-base-zh\\special_tokens_map.json\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'soft_token_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'input_ids': [1, 24, 416, 75, 828, 7, 27, 889, 1399, 203, 280, 5, 277, 628, 367, 944, 24, 47, 1056, 543, 5, 173, 5, 10, 3, 2], 'position_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'masked_positions': [24], 'labels': 'Video-Play'}\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "\n",
    "from paddlenlp.prompt import ManualTemplate\n",
    "from paddlenlp.transformers import AutoTokenizer\n",
    "sample = {'text_a':'给我找一个魔兽世界的比赛视频','labels':'Video-Play'}\n",
    "\n",
    "\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"ernie-3.0-base-zh\")\n",
    "template = ManualTemplate(prompt=\"”{'text': 'text_a'}”这句话的目的是{'mask'}\",\n",
    "                          tokenizer=tokenizer,\n",
    "                          max_length=512)\n",
    "input_dict = template(sample)\n",
    "print(input_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bace75b-03b5-4d41-8242-108790050d4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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